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Data Preparation and Overview

Setup

Standard packages

library(here)
source(here("code", "standard_libraries.R"))

Additional Packages

suppressPackageStartupMessages({
library(DropletUtils)
})

Set parameter

set.seed(100)
bpp <- BiocParallel::MulticoreParam(parallel::detectCores()-1, RNGseed=100)
path <- here::here()

Load Data

Load Cellbender output matrices

Load in the filtered feature barcode matrices from Cellbender of each sample and save them as sce object.

#Load Dataset
rawdata_folder <- paste0(path,"/data/cellbender_data_h5/")

#Get file names
filenames <- list.files(rawdata_folder ,recursive = F, full.names = F,pattern = "\\_filtered.h5$")
filepaths <- paste(rawdata_folder,filenames,sep = "")

#Load data as sce object
sce<-read10xCounts(samples = filepaths, sample.names = filenames,col.names=T,type="HDF5")

#Edit Filenames 
colData(sce)$Sample = sub("\\_w_introns.*", "", sub("cellbender_output_", "", as.character(colData(sce)$Sample)))


sce
class: SingleCellExperiment 
dim: 36601 119652 
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(3): ID Symbol Type
colnames(119652): 1_AAGTACCGTCTCTCAC-1 1_GACTCTCAGGTAACTA-1 ...
  17_AGGTGTTGTGGCATCC-1 17_GCCAGCACACAAGTGG-1
colData names(2): Sample Barcode
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):

Load and add Metadata

#Metadata path
metadata_folder <- paste0(path,"/data/metadata/Metadata_Master.csv")

#Load metadata
metadata_df <-read.csv(metadata_folder, header=TRUE, sep=",")
metadata_df
#Add metadata to sce
colData(sce) <- dplyr::left_join(as.data.frame(colData(sce)),
                                   metadata_df, 
                                   by= c("Sample" = "Sample"),
                                   suffix=c(".x",".y")) %>% 
      #dplyr::select(Sample, Barcode, Diagnosis, sex, Age, Joint.Location, protocol, Pathotype,Krenn total score,) %>% 
      dplyr::select(-one_of("Comments")) %>% #select all except
      DataFrame(row.names=colnames(sce))

#Set Sample names
names(colData(sce))[which(names(colData(sce))=="Sample")]="Orig.Identifier"
names(colData(sce))[which(names(colData(sce))=="ID")]="Sample"

#make row and col names unique
colnames(sce) <- paste0(sce$Sample, ".", sce$Barcode)
rownames(sce) <- paste0(rowData(sce)$ID, ".", rowData(sce)$Symbol)

Exclude Samples

#Dimensions of count matrix
dim(sce)
[1]  36601 119652

Explore dataset

Dimensions of the count matrix

#Exclude sample SynBio130 due to low sequencing depth
#sce <- sce[,sce$Sample!="SynBio130"]
#Feautures/row data
data.frame(colData(sce))
#Droplet details / row data
data.frame(rowData(sce))

Exploratory plots

Histogramm with number of cells

Show the number of cells detected in each sample or joint location before filtering

Per Sample

#Histogramm with number of cells per sample
ggplot(colData(sce), aes(x=Sample))+geom_bar()+ coord_flip()+ ggtitle("Number of cells per sample") + theme_bw()

Version Author Date
b1fc000 sarloet 2024-04-19
5067fa6 sarloet 2024-04-04
bc77174 sarloet 2024-04-03
d291fd3 sarloet 2024-04-02 
data.frame(as.list(table(colData(sce)$Sample)))

Per Joint Location

#Histogramm with number of cells per sample
ggplot(colData(sce), aes(x=Joint.Location))+geom_bar()+ coord_flip()+ ggtitle("Number of cells per sample") + theme_bw()

Version Author Date
b1fc000 sarloet 2024-04-19
5067fa6 sarloet 2024-04-04
bc77174 sarloet 2024-04-03 
data.frame(as.list(table(colData(sce)$Joint.Location)))

Plot number of genes detected per cell

This plot shows cell counts per sample / count occurrence

#Number of genes detected per cell
#Total UMI for a gene versus the number of times detected
genesPerCell <- colSums(counts(sce) > 0)
plot(density(genesPerCell), xlab="Genes per cell", main="Number of genes detected per cell")

Version Author Date
b1fc000 sarloet 2024-04-19
5067fa6 sarloet 2024-04-04
bc77174 sarloet 2024-04-03
d291fd3 sarloet 2024-04-02

Plot transcript capture efficiency

This plot gives an idea about the sequencing depth and if the sequencing has reached saturation or not. Plotted is the total gene count across all cells (x-axis) vs Proportion of cells the gene is detected in (y-axis) where each dot represents a gene.

#transcript_capture_efficiency
#Total UMI for a gene versus the number of times detected
tmpCounts <- counts(sce)

plot(rowSums(tmpCounts),
     rowMeans(tmpCounts > 0),
     log = "x",
     xlab="total number of UMIs",
     ylab="proportion of cells expressing the gene",
     main="Total UMI for a gene vs times detected")

Version Author Date
b1fc000 sarloet 2024-04-19
5067fa6 sarloet 2024-04-04
bc77174 sarloet 2024-04-03
d291fd3 sarloet 2024-04-02

Save the dataset

saveRDS(sce, file =paste0(path,'/output/00_sce_DataPreparation.rds'))

sessionInfo()
R version 4.3.3 (2024-02-29)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.4.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Warsaw
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] DropletUtils_1.22.0         gridExtra_2.3              
 [3] scran_1.30.2                scater_1.30.1              
 [5] scuttle_1.12.0              SingleCellExperiment_1.24.0
 [7] SummarizedExperiment_1.32.0 Biobase_2.62.0             
 [9] GenomicRanges_1.54.1        GenomeInfoDb_1.38.8        
[11] IRanges_2.36.0              S4Vectors_0.40.2           
[13] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
[15] matrixStats_1.3.0           dplyr_1.1.4                
[17] ggplot2_3.5.0               BiocParallel_1.36.0        
[19] here_1.0.1                  workflowr_1.7.1            

loaded via a namespace (and not attached):
 [1] bitops_1.0-7              rlang_1.1.3              
 [3] magrittr_2.0.3            git2r_0.33.0             
 [5] compiler_4.3.3            getPass_0.2-4            
 [7] DelayedMatrixStats_1.24.0 callr_3.7.6              
 [9] vctrs_0.6.5               stringr_1.5.1            
[11] pkgconfig_2.0.3           crayon_1.5.2             
[13] fastmap_1.1.1             XVector_0.42.0           
[15] labeling_0.4.3            utf8_1.2.4               
[17] promises_1.3.0            rmarkdown_2.26           
[19] ps_1.7.6                  ggbeeswarm_0.7.2         
[21] xfun_0.43                 bluster_1.12.0           
[23] zlibbioc_1.48.2           cachem_1.0.8             
[25] beachmat_2.18.1           jsonlite_1.8.8           
[27] highr_0.10                later_1.3.2              
[29] rhdf5filters_1.14.1       DelayedArray_0.28.0      
[31] Rhdf5lib_1.24.2           irlba_2.3.5.1            
[33] parallel_4.3.3            cluster_2.1.6            
[35] R6_2.5.1                  bslib_0.7.0              
[37] stringi_1.8.3             limma_3.58.1             
[39] jquerylib_0.1.4           Rcpp_1.0.12              
[41] knitr_1.45                R.utils_2.12.3           
[43] httpuv_1.6.15             Matrix_1.6-5             
[45] igraph_2.0.3              tidyselect_1.2.1         
[47] rstudioapi_0.16.0         abind_1.4-5              
[49] yaml_2.3.8                viridis_0.6.5            
[51] codetools_0.2-19          processx_3.8.4           
[53] lattice_0.22-5            tibble_3.2.1             
[55] withr_3.0.0               evaluate_0.23            
[57] pillar_1.9.0              whisker_0.4.1            
[59] generics_0.1.3            rprojroot_2.0.4          
[61] RCurl_1.98-1.14           sparseMatrixStats_1.14.0 
[63] munsell_0.5.1             scales_1.3.0             
[65] glue_1.7.0                metapod_1.10.1           
[67] tools_4.3.3               BiocNeighbors_1.20.2     
[69] ScaledMatrix_1.10.0       locfit_1.5-9.9           
[71] fs_1.6.3                  rhdf5_2.46.1             
[73] grid_4.3.3                edgeR_4.0.16             
[75] colorspace_2.1-0          GenomeInfoDbData_1.2.11  
[77] HDF5Array_1.30.1          beeswarm_0.4.0           
[79] BiocSingular_1.18.0       vipor_0.4.7              
[81] cli_3.6.2                 rsvd_1.0.5               
[83] fansi_1.0.6               S4Arrays_1.2.1           
[85] viridisLite_0.4.2         gtable_0.3.4             
[87] R.methodsS3_1.8.2         sass_0.4.9               
[89] digest_0.6.35             SparseArray_1.2.4        
[91] ggrepel_0.9.5             dqrng_0.3.2              
[93] farver_2.1.1              R.oo_1.26.0              
[95] htmltools_0.5.8.1         lifecycle_1.0.4          
[97] httr_1.4.7                statmod_1.5.0